age_gender_net

Introduction

The Age/GenderNet network can be used for image classification. This model was trained to classify ages from 18-75. The model has 2 outputs, one for age: ‘age-conv3’ and another output for gender: ‘prob’.

This sample utilizes the OpenVINO Inference Engine from the OpenVINO Deep Learning Development Toolkit and was tested with the 2020.1 release.

The provided Makefile does the following

Downloads the IR files from the Open Model Zoo
Downloads an aligned face and runs an inference with the age-gender model using the age and gender outputs.

Note: The default image used for inference is a CC0 image and was previously cropped/aligned using the face-detection-retail-0004 sample. When using this network, you will have to use a face detection network like face-detection-retail-0004 to align and crop faces to use as input. This will give you the best results.

Model Information

Inputs

name: ‘input’, shape: [1x3x62x62], Expected color order is BGR.
Outputs
name: ‘age_conv3’, shape: [1, 1, 1, 1] - Estimated age divided by 100.
name: ‘prob’, shape: [1, 2, 1, 1] - Softmax output across 2 type classes [female, male].

Running this Example

make run

Makefile

Provided Makefile describes various targets that help with the above mentioned tasks.

make run or make run_py

Runs a sample application with the network.

make help

Shows makefile possible targets and brief descriptions.

make all

Makes the follow items: deps, data.

make compile_model

Uses the network description and the trained weights files to generate an IR (intermediate representation) format file. This file is later loaded on the Neural Compute Stick where the inferences on the network can be executed.

make install-reqs

Checks required packages that aren’t installed as part of the OpenVINO installation.

make uninstall-reqs

Uninstalls requirements that were installed by the sample program.

make clean

Removes all the temporary and target files that are created by the Makefile.